Overview

Dataset statistics

Number of variables27
Number of observations1868
Missing cells48
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory394.2 KiB
Average record size in memory216.1 B

Variable types

Categorical14
Numeric13

Alerts

Destination has constant value ""Constant
To Area has constant value ""Constant
year has constant value ""Constant
Flight Date has a high cardinality: 265 distinct valuesHigh cardinality
Flight Code has a high cardinality: 88 distinct valuesHigh cardinality
day_convert has a high cardinality: 265 distinct valuesHigh cardinality
Days is highly overall correlated with day_nameHigh correlation
Block is highly overall correlated with Sold and 6 other fieldsHigh correlation
Sold is highly overall correlated with Block and 4 other fieldsHigh correlation
Left is highly overall correlated with Occ.(%) and 1 other fieldsHigh correlation
Occ.(%) is highly overall correlated with Left and 1 other fieldsHigh correlation
Block1 is highly overall correlated with Block and 6 other fieldsHigh correlation
Sold1 is highly overall correlated with Block and 4 other fieldsHigh correlation
Left1 is highly overall correlated with Occ.(%)1High correlation
Occ.(%)1 is highly overall correlated with Left1High correlation
Occ. is highly overall correlated with Left and 1 other fieldsHigh correlation
Netto is highly overall correlated with Block and 7 other fieldsHigh correlation
Profit is highly overall correlated with prıceHigh correlation
prıce is highly overall correlated with Block and 6 other fieldsHigh correlation
Origin is highly overall correlated with Flight CodeHigh correlation
day_name is highly overall correlated with DaysHigh correlation
flight_month is highly overall correlated with seasonHigh correlation
season is highly overall correlated with flight_monthHigh correlation
Flight Code is highly overall correlated with Block and 7 other fieldsHigh correlation
Airline Company is highly overall correlated with Netto and 3 other fieldsHigh correlation
dpt is highly overall correlated with Flight Code and 1 other fieldsHigh correlation
dpt1 is highly overall correlated with Flight Code and 2 other fieldsHigh correlation
Netto Currency is highly overall correlated with Block and 7 other fieldsHigh correlation
Origin is highly imbalanced (52.7%)Imbalance
Profit has 44 (2.4%) missing valuesMissing
Sold has 31 (1.7%) zerosZeros
Left has 950 (50.9%) zerosZeros
Occ.(%) has 32 (1.7%) zerosZeros
Sold1 has 65 (3.5%) zerosZeros
Left1 has 1075 (57.5%) zerosZeros
Occ.(%)1 has 67 (3.6%) zerosZeros
Occ. has 31 (1.7%) zerosZeros

Reproduction

Analysis started2023-02-14 11:22:32.816071
Analysis finished2023-02-14 11:22:53.197550
Duration20.38 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Destination
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
Turkey
1868 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters11208
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTurkey
2nd rowTurkey
3rd rowTurkey
4th rowTurkey
5th rowTurkey

Common Values

ValueCountFrequency (%)
Turkey 1868
100.0%

Length

2023-02-14T14:22:53.242828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:53.330213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
turkey 1868
100.0%

Most occurring characters

ValueCountFrequency (%)
T 1868
16.7%
u 1868
16.7%
r 1868
16.7%
k 1868
16.7%
e 1868
16.7%
y 1868
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9340
83.3%
Uppercase Letter 1868
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 1868
20.0%
r 1868
20.0%
k 1868
20.0%
e 1868
20.0%
y 1868
20.0%
Uppercase Letter
ValueCountFrequency (%)
T 1868
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11208
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1868
16.7%
u 1868
16.7%
r 1868
16.7%
k 1868
16.7%
e 1868
16.7%
y 1868
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1868
16.7%
u 1868
16.7%
r 1868
16.7%
k 1868
16.7%
e 1868
16.7%
y 1868
16.7%

Origin
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
Moscow
1280 
S.Petersburg
140 
Rostov-na-Donu
 
64
Kaliningrad
 
47
Arkhangelsk
 
40
Other values (16)
297 

Length

Max length15
Median length6
Mean length7.3907923
Min length3

Characters and Unicode

Total characters13806
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowArkhangelsk
2nd rowArkhangelsk
3rd rowArkhangelsk
4th rowArkhangelsk
5th rowArkhangelsk

Common Values

ValueCountFrequency (%)
Moscow 1280
68.5%
S.Petersburg 140
 
7.5%
Rostov-na-Donu 64
 
3.4%
Kaliningrad 47
 
2.5%
Arkhangelsk 40
 
2.1%
Voronezh 36
 
1.9%
Mineralnye Vodi 36
 
1.9%
Belgorod 33
 
1.8%
Saratov 30
 
1.6%
Krasnodar 30
 
1.6%
Other values (11) 132
 
7.1%

Length

2023-02-14T14:22:53.411752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moscow 1280
67.2%
s.petersburg 140
 
7.4%
rostov-na-donu 64
 
3.4%
kaliningrad 47
 
2.5%
arkhangelsk 40
 
2.1%
voronezh 36
 
1.9%
mineralnye 36
 
1.9%
vodi 36
 
1.9%
belgorod 33
 
1.7%
saratov 30
 
1.6%
Other values (12) 162
 
8.5%

Most occurring characters

ValueCountFrequency (%)
o 3027
21.9%
s 1566
11.3%
M 1316
9.5%
c 1280
9.3%
w 1280
9.3%
r 687
 
5.0%
e 526
 
3.8%
a 501
 
3.6%
n 478
 
3.5%
g 331
 
2.4%
Other values (29) 2814
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11384
82.5%
Uppercase Letter 2113
 
15.3%
Other Punctuation 145
 
1.1%
Dash Punctuation 128
 
0.9%
Space Separator 36
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3027
26.6%
s 1566
13.8%
c 1280
11.2%
w 1280
11.2%
r 687
 
6.0%
e 526
 
4.6%
a 501
 
4.4%
n 478
 
4.2%
g 331
 
2.9%
t 262
 
2.3%
Other values (12) 1446
12.7%
Uppercase Letter
ValueCountFrequency (%)
M 1316
62.3%
S 184
 
8.7%
P 153
 
7.2%
K 103
 
4.9%
V 85
 
4.0%
D 64
 
3.0%
R 64
 
3.0%
A 40
 
1.9%
B 33
 
1.6%
E 25
 
1.2%
Other values (4) 46
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 145
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13497
97.8%
Common 309
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3027
22.4%
s 1566
11.6%
M 1316
9.8%
c 1280
9.5%
w 1280
9.5%
r 687
 
5.1%
e 526
 
3.9%
a 501
 
3.7%
n 478
 
3.5%
g 331
 
2.5%
Other values (26) 2505
18.6%
Common
ValueCountFrequency (%)
. 145
46.9%
- 128
41.4%
36
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3027
21.9%
s 1566
11.3%
M 1316
9.5%
c 1280
9.3%
w 1280
9.3%
r 687
 
5.0%
e 526
 
3.8%
a 501
 
3.6%
n 478
 
3.5%
g 331
 
2.4%
Other values (29) 2814
20.4%

To Area
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
Antalya
1868 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters13076
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAntalya
2nd rowAntalya
3rd rowAntalya
4th rowAntalya
5th rowAntalya

Common Values

ValueCountFrequency (%)
Antalya 1868
100.0%

Length

2023-02-14T14:22:53.538127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:53.718481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
antalya 1868
100.0%

Most occurring characters

ValueCountFrequency (%)
a 3736
28.6%
A 1868
14.3%
n 1868
14.3%
t 1868
14.3%
l 1868
14.3%
y 1868
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11208
85.7%
Uppercase Letter 1868
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3736
33.3%
n 1868
16.7%
t 1868
16.7%
l 1868
16.7%
y 1868
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 1868
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13076
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3736
28.6%
A 1868
14.3%
n 1868
14.3%
t 1868
14.3%
l 1868
14.3%
y 1868
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3736
28.6%
A 1868
14.3%
n 1868
14.3%
t 1868
14.3%
l 1868
14.3%
y 1868
14.3%

Flight Date
Categorical

Distinct265
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
09.10.2021
 
20
16.10.2021
 
20
02.10.2021
 
18
19.09.2021
 
18
01.10.2021
 
17
Other values (260)
1775 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters18680
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)3.2%

Sample

1st row03.08.2021
2nd row06.08.2021
3rd row10.08.2021
4th row13.08.2021
5th row17.08.2021

Common Values

ValueCountFrequency (%)
09.10.2021 20
 
1.1%
16.10.2021 20
 
1.1%
02.10.2021 18
 
1.0%
19.09.2021 18
 
1.0%
01.10.2021 17
 
0.9%
25.09.2021 17
 
0.9%
12.09.2021 16
 
0.9%
10.10.2021 16
 
0.9%
04.09.2021 16
 
0.9%
11.09.2021 16
 
0.9%
Other values (255) 1694
90.7%

Length

2023-02-14T14:22:53.835290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
09.10.2021 20
 
1.1%
16.10.2021 20
 
1.1%
02.10.2021 18
 
1.0%
19.09.2021 18
 
1.0%
01.10.2021 17
 
0.9%
25.09.2021 17
 
0.9%
07.08.2021 16
 
0.9%
18.09.2021 16
 
0.9%
04.10.2021 16
 
0.9%
27.09.2021 16
 
0.9%
Other values (255) 1694
90.7%

Most occurring characters

ValueCountFrequency (%)
2 4533
24.3%
0 4391
23.5%
. 3736
20.0%
1 3296
17.6%
9 607
 
3.2%
8 601
 
3.2%
7 537
 
2.9%
3 340
 
1.8%
4 236
 
1.3%
6 229
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14944
80.0%
Other Punctuation 3736
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4533
30.3%
0 4391
29.4%
1 3296
22.1%
9 607
 
4.1%
8 601
 
4.0%
7 537
 
3.6%
3 340
 
2.3%
4 236
 
1.6%
6 229
 
1.5%
5 174
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 3736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18680
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4533
24.3%
0 4391
23.5%
. 3736
20.0%
1 3296
17.6%
9 607
 
3.2%
8 601
 
3.2%
7 537
 
2.9%
3 340
 
1.8%
4 236
 
1.3%
6 229
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4533
24.3%
0 4391
23.5%
. 3736
20.0%
1 3296
17.6%
9 607
 
3.2%
8 601
 
3.2%
7 537
 
2.9%
3 340
 
1.8%
4 236
 
1.3%
6 229
 
1.2%

day_name
Categorical

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
Saturday
321 
Sunday
299 
Friday
263 
Wednesday
256 
Monday
249 
Other values (2)
480 

Length

Max length9
Median length8
Mean length7.1381156
Min length6

Characters and Unicode

Total characters13334
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowFriday
3rd rowTuesday
4th rowFriday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Saturday 321
17.2%
Sunday 299
16.0%
Friday 263
14.1%
Wednesday 256
13.7%
Monday 249
13.3%
Tuesday 244
13.1%
Thursday 236
12.6%

Length

2023-02-14T14:22:53.969222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:54.117409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
saturday 321
17.2%
sunday 299
16.0%
friday 263
14.1%
wednesday 256
13.7%
monday 249
13.3%
tuesday 244
13.1%
thursday 236
12.6%

Most occurring characters

ValueCountFrequency (%)
a 2189
16.4%
d 2124
15.9%
y 1868
14.0%
u 1100
8.2%
r 820
 
6.1%
n 804
 
6.0%
e 756
 
5.7%
s 736
 
5.5%
S 620
 
4.6%
T 480
 
3.6%
Other values (7) 1837
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11466
86.0%
Uppercase Letter 1868
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2189
19.1%
d 2124
18.5%
y 1868
16.3%
u 1100
9.6%
r 820
 
7.2%
n 804
 
7.0%
e 756
 
6.6%
s 736
 
6.4%
t 321
 
2.8%
i 263
 
2.3%
Other values (2) 485
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
S 620
33.2%
T 480
25.7%
F 263
14.1%
W 256
13.7%
M 249
13.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 13334
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2189
16.4%
d 2124
15.9%
y 1868
14.0%
u 1100
8.2%
r 820
 
6.1%
n 804
 
6.0%
e 756
 
5.7%
s 736
 
5.5%
S 620
 
4.6%
T 480
 
3.6%
Other values (7) 1837
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2189
16.4%
d 2124
15.9%
y 1868
14.0%
u 1100
8.2%
r 820
 
6.1%
n 804
 
6.0%
e 756
 
5.7%
s 736
 
5.5%
S 620
 
4.6%
T 480
 
3.6%
Other values (7) 1837
13.8%

flight_month
Categorical

Distinct11
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
September
427 
August
421 
October
382 
July
338 
November
74 
Other values (6)
226 

Length

Max length9
Median length7
Mean length6.5711991
Min length4

Characters and Unicode

Total characters12275
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAugust
2nd rowAugust
3rd rowAugust
4th rowAugust
5th rowAugust

Common Values

ValueCountFrequency (%)
September 427
22.9%
August 421
22.5%
October 382
20.4%
July 338
18.1%
November 74
 
4.0%
March 57
 
3.1%
April 48
 
2.6%
June 43
 
2.3%
January 33
 
1.8%
February 23
 
1.2%

Length

2023-02-14T14:22:54.310917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
september 427
22.9%
august 421
22.5%
october 382
20.4%
july 338
18.1%
november 74
 
4.0%
march 57
 
3.1%
april 48
 
2.6%
june 43
 
2.3%
january 33
 
1.8%
february 23
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 1943
15.8%
u 1279
 
10.4%
t 1230
 
10.0%
r 1089
 
8.9%
b 928
 
7.6%
m 523
 
4.3%
p 475
 
3.9%
A 469
 
3.8%
c 461
 
3.8%
o 456
 
3.7%
Other values (16) 3422
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10407
84.8%
Uppercase Letter 1868
 
15.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1943
18.7%
u 1279
12.3%
t 1230
11.8%
r 1089
10.5%
b 928
8.9%
m 523
 
5.0%
p 475
 
4.6%
c 461
 
4.4%
o 456
 
4.4%
s 421
 
4.0%
Other values (8) 1602
15.4%
Uppercase Letter
ValueCountFrequency (%)
A 469
25.1%
S 427
22.9%
J 414
22.2%
O 382
20.4%
N 74
 
4.0%
M 57
 
3.1%
F 23
 
1.2%
D 22
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 12275
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1943
15.8%
u 1279
 
10.4%
t 1230
 
10.0%
r 1089
 
8.9%
b 928
 
7.6%
m 523
 
4.3%
p 475
 
3.9%
A 469
 
3.8%
c 461
 
3.8%
o 456
 
3.7%
Other values (16) 3422
27.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1943
15.8%
u 1279
 
10.4%
t 1230
 
10.0%
r 1089
 
8.9%
b 928
 
7.6%
m 523
 
4.3%
p 475
 
3.9%
A 469
 
3.8%
c 461
 
3.8%
o 456
 
3.7%
Other values (16) 3422
27.9%

season
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
Fall
883 
Summer
802 
Spring
105 
Winter
 
78

Length

Max length6
Median length6
Mean length5.0546039
Min length4

Characters and Unicode

Total characters9442
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowSummer
3rd rowSummer
4th rowSummer
5th rowSummer

Common Values

ValueCountFrequency (%)
Fall 883
47.3%
Summer 802
42.9%
Spring 105
 
5.6%
Winter 78
 
4.2%

Length

2023-02-14T14:22:54.395154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:54.548006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
fall 883
47.3%
summer 802
42.9%
spring 105
 
5.6%
winter 78
 
4.2%

Most occurring characters

ValueCountFrequency (%)
l 1766
18.7%
m 1604
17.0%
r 985
10.4%
S 907
9.6%
F 883
9.4%
a 883
9.4%
e 880
9.3%
u 802
8.5%
i 183
 
1.9%
n 183
 
1.9%
Other values (4) 366
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7574
80.2%
Uppercase Letter 1868
 
19.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 1766
23.3%
m 1604
21.2%
r 985
13.0%
a 883
11.7%
e 880
11.6%
u 802
10.6%
i 183
 
2.4%
n 183
 
2.4%
p 105
 
1.4%
g 105
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
S 907
48.6%
F 883
47.3%
W 78
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 9442
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 1766
18.7%
m 1604
17.0%
r 985
10.4%
S 907
9.6%
F 883
9.4%
a 883
9.4%
e 880
9.3%
u 802
8.5%
i 183
 
1.9%
n 183
 
1.9%
Other values (4) 366
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 1766
18.7%
m 1604
17.0%
r 985
10.4%
S 907
9.6%
F 883
9.4%
a 883
9.4%
e 880
9.3%
u 802
8.5%
i 183
 
1.9%
n 183
 
1.9%
Other values (4) 366
 
3.9%

year
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
2021
1868 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters7472
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 1868
100.0%

Length

2023-02-14T14:22:54.706502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:54.863998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2021 1868
100.0%

Most occurring characters

ValueCountFrequency (%)
2 3736
50.0%
0 1868
25.0%
1 1868
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7472
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3736
50.0%
0 1868
25.0%
1 1868
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3736
50.0%
0 1868
25.0%
1 1868
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3736
50.0%
0 1868
25.0%
1 1868
25.0%

Flight Code
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct88
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
RL 7709
249 
RL 7711
218 
TK 212
200 
RL 7707
117 
TK 4002
100 
Other values (83)
984 

Length

Max length7
Median length7
Mean length6.877409
Min length6

Characters and Unicode

Total characters12847
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)1.1%

Sample

1st rowU6 1137
2nd rowU6 1137
3rd rowU6 1137
4th rowU6 1137
5th rowU6 1137

Common Values

ValueCountFrequency (%)
RL 7709 249
13.3%
RL 7711 218
 
11.7%
TK 212 200
 
10.7%
RL 7707 117
 
6.3%
TK 4002 100
 
5.4%
U6 3001 94
 
5.0%
SU 2142 89
 
4.8%
SU 6693 74
 
4.0%
RL 7575 68
 
3.6%
RL 8073 46
 
2.5%
Other values (78) 613
32.8%

Length

2023-02-14T14:22:54.961236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rl 887
23.7%
tk 300
 
8.0%
7709 249
 
6.7%
u6 238
 
6.4%
7711 218
 
5.8%
wz 213
 
5.7%
212 200
 
5.4%
su 163
 
4.4%
7707 117
 
3.1%
4002 100
 
2.7%
Other values (86) 1051
28.1%

Most occurring characters

ValueCountFrequency (%)
1868
14.5%
7 1780
13.9%
1 1366
10.6%
0 1324
10.3%
R 887
 
6.9%
L 887
 
6.9%
2 717
 
5.6%
4 469
 
3.7%
6 465
 
3.6%
9 417
 
3.2%
Other values (15) 2667
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7534
58.6%
Uppercase Letter 3445
26.8%
Space Separator 1868
 
14.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 887
25.7%
L 887
25.7%
U 401
11.6%
K 300
 
8.7%
T 300
 
8.7%
Z 215
 
6.2%
W 213
 
6.2%
S 163
 
4.7%
N 53
 
1.5%
E 11
 
0.3%
Other values (4) 15
 
0.4%
Decimal Number
ValueCountFrequency (%)
7 1780
23.6%
1 1366
18.1%
0 1324
17.6%
2 717
9.5%
4 469
 
6.2%
6 465
 
6.2%
9 417
 
5.5%
3 406
 
5.4%
5 347
 
4.6%
8 243
 
3.2%
Space Separator
ValueCountFrequency (%)
1868
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9402
73.2%
Latin 3445
 
26.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 887
25.7%
L 887
25.7%
U 401
11.6%
K 300
 
8.7%
T 300
 
8.7%
Z 215
 
6.2%
W 213
 
6.2%
S 163
 
4.7%
N 53
 
1.5%
E 11
 
0.3%
Other values (4) 15
 
0.4%
Common
ValueCountFrequency (%)
1868
19.9%
7 1780
18.9%
1 1366
14.5%
0 1324
14.1%
2 717
 
7.6%
4 469
 
5.0%
6 465
 
4.9%
9 417
 
4.4%
3 406
 
4.3%
5 347
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1868
14.5%
7 1780
13.9%
1 1366
10.6%
0 1324
10.3%
R 887
 
6.9%
L 887
 
6.9%
2 717
 
5.6%
4 469
 
3.7%
6 465
 
3.6%
9 417
 
3.2%
Other values (15) 2667
20.8%

Days
Real number (ℝ)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1664882
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:55.047268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0254437
Coefficient of variation (CV)0.48612731
Kurtosis-1.2835544
Mean4.1664882
Median Absolute Deviation (MAD)2
Skewness-0.12009845
Sum7783
Variance4.1024222
MonotonicityNot monotonic
2023-02-14T14:22:55.108402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 321
17.2%
7 299
16.0%
5 263
14.1%
3 256
13.7%
1 249
13.3%
2 244
13.1%
4 236
12.6%
ValueCountFrequency (%)
1 249
13.3%
2 244
13.1%
3 256
13.7%
4 236
12.6%
5 263
14.1%
6 321
17.2%
7 299
16.0%
ValueCountFrequency (%)
7 299
16.0%
6 321
17.2%
5 263
14.1%
4 236
12.6%
3 256
13.7%
2 244
13.1%
1 249
13.3%

Airline Company
Categorical

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
Royal Flight
887 
Turkish Airlines
300 
Ural Airlines
238 
Red Wings Airlines
213 
Aeroflot
163 
Other values (4)
 
67

Length

Max length18
Median length16
Mean length12.994111
Min length5

Characters and Unicode

Total characters24273
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowUral Airlines
2nd rowUral Airlines
3rd rowUral Airlines
4th rowUral Airlines
5th rowUral Airlines

Common Values

ValueCountFrequency (%)
Royal Flight 887
47.5%
Turkish Airlines 300
 
16.1%
Ural Airlines 238
 
12.7%
Red Wings Airlines 213
 
11.4%
Aeroflot 163
 
8.7%
Nord Wind 53
 
2.8%
Pegas Fly 11
 
0.6%
Azur Air 2
 
0.1%
Yamal 1
 
0.1%

Length

2023-02-14T14:22:55.188751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:55.342160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
royal 887
23.4%
flight 887
23.4%
airlines 751
19.8%
turkish 300
 
7.9%
ural 238
 
6.3%
red 213
 
5.6%
wings 213
 
5.6%
aeroflot 163
 
4.3%
nord 53
 
1.4%
wind 53
 
1.4%
Other values (5) 27
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i 2957
 
12.2%
l 2938
 
12.1%
1917
 
7.9%
r 1509
 
6.2%
s 1275
 
5.3%
o 1266
 
5.2%
h 1187
 
4.9%
a 1138
 
4.7%
e 1138
 
4.7%
g 1111
 
4.6%
Other values (18) 7837
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18571
76.5%
Uppercase Letter 3785
 
15.6%
Space Separator 1917
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2957
15.9%
l 2938
15.8%
r 1509
8.1%
s 1275
6.9%
o 1266
6.8%
h 1187
 
6.4%
a 1138
 
6.1%
e 1138
 
6.1%
g 1111
 
6.0%
t 1050
 
5.7%
Other values (8) 3002
16.2%
Uppercase Letter
ValueCountFrequency (%)
R 1100
29.1%
A 918
24.3%
F 898
23.7%
T 300
 
7.9%
W 266
 
7.0%
U 238
 
6.3%
N 53
 
1.4%
P 11
 
0.3%
Y 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1917
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22356
92.1%
Common 1917
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2957
13.2%
l 2938
13.1%
r 1509
 
6.7%
s 1275
 
5.7%
o 1266
 
5.7%
h 1187
 
5.3%
a 1138
 
5.1%
e 1138
 
5.1%
g 1111
 
5.0%
R 1100
 
4.9%
Other values (17) 6737
30.1%
Common
ValueCountFrequency (%)
1917
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2957
 
12.2%
l 2938
 
12.1%
1917
 
7.9%
r 1509
 
6.2%
s 1275
 
5.3%
o 1266
 
5.2%
h 1187
 
4.9%
a 1138
 
4.7%
e 1138
 
4.7%
g 1111
 
4.6%
Other values (18) 7837
32.3%

dpt
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
afternoon
814 
night
698 
morning
341 
evening
 
15

Length

Max length9
Median length7
Mean length7.124197
Min length5

Characters and Unicode

Total characters13308
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowafternoon
2nd rowmorning
3rd rowafternoon
4th rowmorning
5th rowafternoon

Common Values

ValueCountFrequency (%)
afternoon 814
43.6%
night 698
37.4%
morning 341
18.3%
evening 15
 
0.8%

Length

2023-02-14T14:22:55.493398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:55.625986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
afternoon 814
43.6%
night 698
37.4%
morning 341
18.3%
evening 15
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 3038
22.8%
o 1969
14.8%
t 1512
11.4%
r 1155
 
8.7%
i 1054
 
7.9%
g 1054
 
7.9%
e 844
 
6.3%
a 814
 
6.1%
f 814
 
6.1%
h 698
 
5.2%
Other values (2) 356
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13308
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3038
22.8%
o 1969
14.8%
t 1512
11.4%
r 1155
 
8.7%
i 1054
 
7.9%
g 1054
 
7.9%
e 844
 
6.3%
a 814
 
6.1%
f 814
 
6.1%
h 698
 
5.2%
Other values (2) 356
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 13308
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3038
22.8%
o 1969
14.8%
t 1512
11.4%
r 1155
 
8.7%
i 1054
 
7.9%
g 1054
 
7.9%
e 844
 
6.3%
a 814
 
6.1%
f 814
 
6.1%
h 698
 
5.2%
Other values (2) 356
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3038
22.8%
o 1969
14.8%
t 1512
11.4%
r 1155
 
8.7%
i 1054
 
7.9%
g 1054
 
7.9%
e 844
 
6.3%
a 814
 
6.1%
f 814
 
6.1%
h 698
 
5.2%
Other values (2) 356
 
2.7%

Block
Real number (ℝ)

Distinct69
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.02944
Minimum1
Maximum492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:55.852564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median214.5
Q3235
95-th percentile480
Maximum492
Range491
Interquartile range (IQR)220

Descriptive statistics

Standard deviation164.82552
Coefficient of variation (CV)0.94170168
Kurtosis-0.74692255
Mean175.02944
Median Absolute Deviation (MAD)184.5
Skewness0.66470634
Sum326955
Variance27167.452
MonotonicityNot monotonic
2023-02-14T14:22:55.946275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 408
21.8%
30 215
11.5%
15 163
 
8.7%
235 142
 
7.6%
14 131
 
7.0%
478 126
 
6.7%
4 105
 
5.6%
330 63
 
3.4%
189 57
 
3.1%
13 46
 
2.5%
Other values (59) 412
22.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 4
 
0.2%
3 4
 
0.2%
4 105
5.6%
5 6
 
0.3%
6 4
 
0.2%
7 7
 
0.4%
8 10
 
0.5%
9 7
 
0.4%
10 18
 
1.0%
ValueCountFrequency (%)
492 7
 
0.4%
490 5
 
0.3%
489 3
 
0.2%
488 5
 
0.3%
487 7
 
0.4%
486 4
 
0.2%
485 7
 
0.4%
484 9
0.5%
483 7
 
0.4%
482 18
1.0%

Sold
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct244
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.70289
Minimum0
Maximum492
Zeros31
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:56.022111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q114
median110
Q3220
95-th percentile479
Maximum492
Range492
Interquartile range (IQR)206

Descriptive statistics

Standard deviation161.29212
Coefficient of variation (CV)1.0163149
Kurtosis-0.48700979
Mean158.70289
Median Absolute Deviation (MAD)102
Skewness0.8407334
Sum296457
Variance26015.147
MonotonicityNot monotonic
2023-02-14T14:22:56.167439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 164
 
8.8%
220 153
 
8.2%
15 108
 
5.8%
14 107
 
5.7%
4 75
 
4.0%
13 63
 
3.4%
478 58
 
3.1%
12 47
 
2.5%
235 40
 
2.1%
218 36
 
1.9%
Other values (234) 1017
54.4%
ValueCountFrequency (%)
0 31
1.7%
1 5
 
0.3%
2 19
 
1.0%
3 22
 
1.2%
4 75
4.0%
5 6
 
0.3%
6 13
 
0.7%
7 9
 
0.5%
8 14
 
0.7%
9 17
 
0.9%
ValueCountFrequency (%)
492 2
 
0.1%
490 2
 
0.1%
489 2
 
0.1%
488 2
 
0.1%
487 7
0.4%
486 4
 
0.2%
485 5
0.3%
484 5
0.3%
483 7
0.4%
482 12
0.6%

Left
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct160
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.326552
Minimum-4
Maximum478
Zeros950
Zeros (%)50.9%
Negative8
Negative (%)0.4%
Memory size14.7 KiB
2023-02-14T14:22:56.277046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile0
Q10
median0
Q34
95-th percentile121
Maximum478
Range482
Interquartile range (IQR)4

Descriptive statistics

Standard deviation52.513639
Coefficient of variation (CV)3.2164561
Kurtosis28.756237
Mean16.326552
Median Absolute Deviation (MAD)0
Skewness4.8914992
Sum30498
Variance2757.6823
MonotonicityNot monotonic
2023-02-14T14:22:56.360108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 950
50.9%
1 212
 
11.3%
2 148
 
7.9%
3 76
 
4.1%
4 64
 
3.4%
5 39
 
2.1%
8 27
 
1.4%
6 25
 
1.3%
7 16
 
0.9%
14 16
 
0.9%
Other values (150) 295
 
15.8%
ValueCountFrequency (%)
-4 1
 
0.1%
-2 4
 
0.2%
-1 3
 
0.2%
0 950
50.9%
1 212
 
11.3%
2 148
 
7.9%
3 76
 
4.1%
4 64
 
3.4%
5 39
 
2.1%
6 25
 
1.3%
ValueCountFrequency (%)
478 4
0.2%
476 1
 
0.1%
443 1
 
0.1%
374 1
 
0.1%
371 1
 
0.1%
354 1
 
0.1%
330 6
0.3%
318 1
 
0.1%
315 1
 
0.1%
283 1
 
0.1%

Occ.(%)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.152034
Minimum0
Maximum101
Zeros32
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:56.508231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.35
Q197
median100
Q3100
95-th percentile100
Maximum101
Range101
Interquartile range (IQR)3

Descriptive statistics

Standard deviation21.209502
Coefficient of variation (CV)0.2326827
Kurtosis8.3028974
Mean91.152034
Median Absolute Deviation (MAD)0
Skewness-2.9698753
Sum170272
Variance449.84297
MonotonicityNot monotonic
2023-02-14T14:22:56.616464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1100
58.9%
99 167
 
8.9%
98 80
 
4.3%
97 59
 
3.2%
93 53
 
2.8%
0 32
 
1.7%
80 23
 
1.2%
96 20
 
1.1%
75 20
 
1.1%
87 19
 
1.0%
Other values (84) 295
 
15.8%
ValueCountFrequency (%)
0 32
1.7%
1 1
 
0.1%
2 1
 
0.1%
3 2
 
0.1%
4 2
 
0.1%
5 2
 
0.1%
6 1
 
0.1%
7 2
 
0.1%
9 1
 
0.1%
10 4
 
0.2%
ValueCountFrequency (%)
101 2
 
0.1%
100 1100
58.9%
99 167
 
8.9%
98 80
 
4.3%
97 59
 
3.2%
96 20
 
1.1%
95 12
 
0.6%
94 15
 
0.8%
93 53
 
2.8%
92 6
 
0.3%

dpt1
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
morning
754 
evening
616 
afternoon
438 
night
 
60

Length

Max length9
Median length7
Mean length7.4047109
Min length5

Characters and Unicode

Total characters13832
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmorning
2nd rownight
3rd rowmorning
4th rownight
5th rowmorning

Common Values

ValueCountFrequency (%)
morning 754
40.4%
evening 616
33.0%
afternoon 438
23.4%
night 60
 
3.2%

Length

2023-02-14T14:22:56.688170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:56.761151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
morning 754
40.4%
evening 616
33.0%
afternoon 438
23.4%
night 60
 
3.2%

Most occurring characters

ValueCountFrequency (%)
n 3676
26.6%
e 1670
12.1%
o 1630
11.8%
i 1430
 
10.3%
g 1430
 
10.3%
r 1192
 
8.6%
m 754
 
5.5%
v 616
 
4.5%
t 498
 
3.6%
a 438
 
3.2%
Other values (2) 498
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13832
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3676
26.6%
e 1670
12.1%
o 1630
11.8%
i 1430
 
10.3%
g 1430
 
10.3%
r 1192
 
8.6%
m 754
 
5.5%
v 616
 
4.5%
t 498
 
3.6%
a 438
 
3.2%
Other values (2) 498
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 13832
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3676
26.6%
e 1670
12.1%
o 1630
11.8%
i 1430
 
10.3%
g 1430
 
10.3%
r 1192
 
8.6%
m 754
 
5.5%
v 616
 
4.5%
t 498
 
3.6%
a 438
 
3.2%
Other values (2) 498
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3676
26.6%
e 1670
12.1%
o 1630
11.8%
i 1430
 
10.3%
g 1430
 
10.3%
r 1192
 
8.6%
m 754
 
5.5%
v 616
 
4.5%
t 498
 
3.6%
a 438
 
3.2%
Other values (2) 498
 
3.6%

Block1
Real number (ℝ)

Distinct70
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.43897
Minimum0
Maximum492
Zeros14
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:56.858314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q115
median193
Q3235
95-th percentile478
Maximum492
Range492
Interquartile range (IQR)220

Descriptive statistics

Standard deviation164.49943
Coefficient of variation (CV)0.94301994
Kurtosis-0.73502945
Mean174.43897
Median Absolute Deviation (MAD)163
Skewness0.667116
Sum325852
Variance27060.062
MonotonicityNot monotonic
2023-02-14T14:22:56.967912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 408
21.8%
30 212
11.3%
478 202
10.8%
14 156
 
8.4%
235 153
 
8.2%
15 139
 
7.4%
4 101
 
5.4%
189 60
 
3.2%
330 57
 
3.1%
90 38
 
2.0%
Other values (60) 342
18.3%
ValueCountFrequency (%)
0 14
 
0.7%
1 3
 
0.2%
2 4
 
0.2%
3 6
 
0.3%
4 101
5.4%
5 5
 
0.3%
6 7
 
0.4%
7 5
 
0.3%
8 5
 
0.3%
9 18
 
1.0%
ValueCountFrequency (%)
492 6
0.3%
491 3
0.2%
490 3
0.2%
489 3
0.2%
488 5
0.3%
487 5
0.3%
486 4
0.2%
485 3
0.2%
484 3
0.2%
483 1
 
0.1%

Sold1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct250
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.36242
Minimum0
Maximum494
Zeros65
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:57.056159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q114
median109
Q3220
95-th percentile478.65
Maximum494
Range494
Interquartile range (IQR)206

Descriptive statistics

Standard deviation161.04409
Coefficient of variation (CV)1.0299411
Kurtosis-0.45456876
Mean156.36242
Median Absolute Deviation (MAD)104
Skewness0.85211389
Sum292085
Variance25935.199
MonotonicityNot monotonic
2023-02-14T14:22:57.201884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 191
 
10.2%
30 157
 
8.4%
14 121
 
6.5%
478 108
 
5.8%
15 92
 
4.9%
4 73
 
3.9%
235 70
 
3.7%
0 65
 
3.5%
13 49
 
2.6%
479 34
 
1.8%
Other values (240) 908
48.6%
ValueCountFrequency (%)
0 65
3.5%
1 9
 
0.5%
2 18
 
1.0%
3 30
1.6%
4 73
3.9%
5 11
 
0.6%
6 12
 
0.6%
7 8
 
0.4%
8 13
 
0.7%
9 19
 
1.0%
ValueCountFrequency (%)
494 1
 
0.1%
493 1
 
0.1%
492 1
 
0.1%
491 3
0.2%
490 4
0.2%
489 3
0.2%
488 2
0.1%
487 2
0.1%
486 1
 
0.1%
485 1
 
0.1%

Left1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct167
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.076552
Minimum-6
Maximum478
Zeros1075
Zeros (%)57.5%
Negative70
Negative (%)3.7%
Memory size14.7 KiB
2023-02-14T14:22:57.375683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile0
Q10
median0
Q33
95-th percentile129.65
Maximum478
Range484
Interquartile range (IQR)3

Descriptive statistics

Standard deviation58.526113
Coefficient of variation (CV)3.2376811
Kurtosis24.314412
Mean18.076552
Median Absolute Deviation (MAD)0
Skewness4.5814641
Sum33767
Variance3425.3059
MonotonicityNot monotonic
2023-02-14T14:22:57.485285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1075
57.5%
1 159
 
8.5%
2 81
 
4.3%
3 52
 
2.8%
-1 43
 
2.3%
4 35
 
1.9%
6 26
 
1.4%
5 24
 
1.3%
8 17
 
0.9%
14 14
 
0.7%
Other values (157) 342
 
18.3%
ValueCountFrequency (%)
-6 1
 
0.1%
-5 1
 
0.1%
-4 3
 
0.2%
-3 8
 
0.4%
-2 14
 
0.7%
-1 43
 
2.3%
0 1075
57.5%
1 159
 
8.5%
2 81
 
4.3%
3 52
 
2.8%
ValueCountFrequency (%)
478 1
0.1%
473 1
0.1%
470 1
0.1%
468 2
0.1%
464 1
0.1%
457 1
0.1%
452 1
0.1%
448 1
0.1%
440 1
0.1%
381 1
0.1%

Occ.(%)1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.835118
Minimum0
Maximum107
Zeros67
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:58.140795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.35
Q193
median100
Q3100
95-th percentile100
Maximum107
Range107
Interquartile range (IQR)7

Descriptive statistics

Standard deviation25.420968
Coefficient of variation (CV)0.28615899
Kurtosis5.5949075
Mean88.835118
Median Absolute Deviation (MAD)0
Skewness-2.5825246
Sum165944
Variance646.22561
MonotonicityNot monotonic
2023-02-14T14:22:58.244622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1185
63.4%
99 75
 
4.0%
0 67
 
3.6%
93 59
 
3.2%
97 49
 
2.6%
98 37
 
2.0%
75 26
 
1.4%
101 17
 
0.9%
96 16
 
0.9%
87 15
 
0.8%
Other values (80) 322
 
17.2%
ValueCountFrequency (%)
0 67
3.6%
1 6
 
0.3%
2 4
 
0.2%
3 3
 
0.2%
4 2
 
0.1%
5 3
 
0.2%
6 4
 
0.2%
7 5
 
0.3%
8 2
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
107 1
 
0.1%
102 1
 
0.1%
101 17
 
0.9%
100 1185
63.4%
99 75
 
4.0%
98 37
 
2.0%
97 49
 
2.6%
96 16
 
0.9%
95 7
 
0.4%
94 10
 
0.5%

Occ.
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct307
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.125418
Minimum0
Maximum100.91
Zeros31
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:58.447138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.521
Q196.67
median100
Q3100
95-th percentile100
Maximum100.91
Range100.91
Interquartile range (IQR)3.33

Descriptive statistics

Standard deviation21.195752
Coefficient of variation (CV)0.23259978
Kurtosis8.3042491
Mean91.125418
Median Absolute Deviation (MAD)0
Skewness-2.9701157
Sum170222.28
Variance449.25991
MonotonicityNot monotonic
2023-02-14T14:22:58.599873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 950
50.9%
99.09 42
 
2.2%
99.79 39
 
2.1%
99.55 36
 
1.9%
93.33 35
 
1.9%
96.67 32
 
1.7%
0 31
 
1.7%
99.58 26
 
1.4%
98.18 23
 
1.2%
99.57 20
 
1.1%
Other values (297) 634
33.9%
ValueCountFrequency (%)
0 31
1.7%
0.45 1
 
0.1%
1.36 1
 
0.1%
1.82 1
 
0.1%
3.17 1
 
0.1%
3.4 1
 
0.1%
4.09 1
 
0.1%
4.23 1
 
0.1%
4.55 1
 
0.1%
4.76 1
 
0.1%
ValueCountFrequency (%)
100.91 1
 
0.1%
100.83 1
 
0.1%
100.43 1
 
0.1%
100.42 3
 
0.2%
100.21 2
 
0.1%
100 950
50.9%
99.8 2
 
0.1%
99.79 39
 
2.1%
99.7 7
 
0.4%
99.59 13
 
0.7%

Netto
Real number (ℝ)

Distinct149
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.80257
Minimum50
Maximum475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-02-14T14:22:58.779650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile158.35
Q1191
median226.5
Q3276
95-th percentile475
Maximum475
Range425
Interquartile range (IQR)85

Descriptive statistics

Standard deviation80.137739
Coefficient of variation (CV)0.3273566
Kurtosis1.5886385
Mean244.80257
Median Absolute Deviation (MAD)36.5
Skewness1.4115463
Sum457291.21
Variance6422.0572
MonotonicityNot monotonic
2023-02-14T14:22:58.868413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320 200
 
10.7%
191 104
 
5.6%
475 100
 
5.4%
394 89
 
4.8%
276 76
 
4.1%
192 67
 
3.6%
233 62
 
3.3%
237 49
 
2.6%
190 45
 
2.4%
229 44
 
2.4%
Other values (139) 1032
55.2%
ValueCountFrequency (%)
50 1
0.1%
75 1
0.1%
80 1
0.1%
95 1
0.1%
100 1
0.1%
124 1
0.1%
131 1
0.1%
133 1
0.1%
140 1
0.1%
142 2
0.1%
ValueCountFrequency (%)
475 100
5.4%
394 89
4.8%
351 1
 
0.1%
320 200
10.7%
282 1
 
0.1%
278 8
 
0.4%
277 5
 
0.3%
276 76
 
4.1%
275 4
 
0.2%
274 2
 
0.1%

Netto Currency
Categorical

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.2%
Memory size14.7 KiB
EUR
1562 
USD
302 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5592
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUR
2nd rowEUR
3rd rowEUR
4th rowEUR
5th rowEUR

Common Values

ValueCountFrequency (%)
EUR 1562
83.6%
USD 302
 
16.2%
(Missing) 4
 
0.2%

Length

2023-02-14T14:22:58.966355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:22:59.043297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
eur 1562
83.8%
usd 302
 
16.2%

Most occurring characters

ValueCountFrequency (%)
U 1864
33.3%
E 1562
27.9%
R 1562
27.9%
S 302
 
5.4%
D 302
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5592
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 1864
33.3%
E 1562
27.9%
R 1562
27.9%
S 302
 
5.4%
D 302
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5592
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 1864
33.3%
E 1562
27.9%
R 1562
27.9%
S 302
 
5.4%
D 302
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 1864
33.3%
E 1562
27.9%
R 1562
27.9%
S 302
 
5.4%
D 302
 
5.4%

Profit
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1771
Distinct (%)97.1%
Missing44
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean48.070291
Minimum-329.46
Maximum681.1
Zeros0
Zeros (%)0.0%
Negative555
Negative (%)29.7%
Memory size14.7 KiB
2023-02-14T14:22:59.133197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-329.46
5-th percentile-60.6775
Q1-9.6875
median34.925
Q382.05
95-th percentile215.863
Maximum681.1
Range1010.56
Interquartile range (IQR)91.7375

Descriptive statistics

Standard deviation89.938733
Coefficient of variation (CV)1.8709838
Kurtosis4.664656
Mean48.070291
Median Absolute Deviation (MAD)45.935
Skewness1.337311
Sum87680.21
Variance8088.9757
MonotonicityNot monotonic
2023-02-14T14:22:59.285083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.19 3
 
0.2%
19.43 3
 
0.2%
51.78 3
 
0.2%
54.42 2
 
0.1%
28.4 2
 
0.1%
52.4 2
 
0.1%
13.5 2
 
0.1%
-21.87 2
 
0.1%
2.31 2
 
0.1%
47.57 2
 
0.1%
Other values (1761) 1801
96.4%
(Missing) 44
 
2.4%
ValueCountFrequency (%)
-329.46 1
0.1%
-252.83 1
0.1%
-211.82 1
0.1%
-210.89 1
0.1%
-203.98 1
0.1%
-166.76 1
0.1%
-154.32 1
0.1%
-152.89 1
0.1%
-150.25 1
0.1%
-148.74 1
0.1%
ValueCountFrequency (%)
681.1 1
0.1%
610.02 1
0.1%
587.59 1
0.1%
530.72 1
0.1%
522.21 1
0.1%
473.61 1
0.1%
460.45 1
0.1%
456.37 1
0.1%
402.03 1
0.1%
390.79 1
0.1%

prıce
Real number (ℝ)

Distinct1821
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.74059
Minimum-68.46
Maximum905.1
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.1%
Memory size14.7 KiB
2023-02-14T14:22:59.355846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-68.46
5-th percentile129.6215
Q1200.54
median265.78
Q3369.02
95-th percentile517.717
Maximum905.1
Range973.56
Interquartile range (IQR)168.48

Descriptive statistics

Standard deviation126.65168
Coefficient of variation (CV)0.43412432
Kurtosis1.1741919
Mean291.74059
Median Absolute Deviation (MAD)82.005
Skewness0.9132575
Sum544971.42
Variance16040.649
MonotonicityNot monotonic
2023-02-14T14:22:59.503584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
475 4
 
0.2%
163 3
 
0.2%
191 3
 
0.2%
279.43 3
 
0.2%
153 3
 
0.2%
197 3
 
0.2%
154 3
 
0.2%
198 2
 
0.1%
261.69 2
 
0.1%
193.85 2
 
0.1%
Other values (1811) 1840
98.5%
ValueCountFrequency (%)
-68.46 1
0.1%
-42.83 1
0.1%
6.11 1
0.1%
17.11 1
0.1%
22.75 1
0.1%
28.26 1
0.1%
33.48 1
0.1%
42.02 1
0.1%
46.18 1
0.1%
50 1
0.1%
ValueCountFrequency (%)
905.1 1
0.1%
839.02 1
0.1%
823.59 1
0.1%
814.08 1
0.1%
802.73 1
0.1%
753.72 1
0.1%
751.21 1
0.1%
740.03 1
0.1%
737.8 1
0.1%
731.23 1
0.1%

day_convert
Categorical

Distinct265
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
2021-10-09
 
20
2021-10-16
 
20
2021-10-02
 
18
2021-09-19
 
18
2021-10-01
 
17
Other values (260)
1775 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters18680
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)3.2%

Sample

1st row2021-08-03
2nd row2021-08-06
3rd row2021-08-10
4th row2021-08-13
5th row2021-08-17

Common Values

ValueCountFrequency (%)
2021-10-09 20
 
1.1%
2021-10-16 20
 
1.1%
2021-10-02 18
 
1.0%
2021-09-19 18
 
1.0%
2021-10-01 17
 
0.9%
2021-09-25 17
 
0.9%
2021-09-12 16
 
0.9%
2021-10-10 16
 
0.9%
2021-09-04 16
 
0.9%
2021-09-11 16
 
0.9%
Other values (255) 1694
90.7%

Length

2023-02-14T14:22:59.571589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-10-09 20
 
1.1%
2021-10-16 20
 
1.1%
2021-10-02 18
 
1.0%
2021-09-19 18
 
1.0%
2021-10-01 17
 
0.9%
2021-09-25 17
 
0.9%
2021-08-07 16
 
0.9%
2021-09-18 16
 
0.9%
2021-10-04 16
 
0.9%
2021-09-27 16
 
0.9%
Other values (255) 1694
90.7%

Most occurring characters

ValueCountFrequency (%)
2 4533
24.3%
0 4391
23.5%
- 3736
20.0%
1 3296
17.6%
9 607
 
3.2%
8 601
 
3.2%
7 537
 
2.9%
3 340
 
1.8%
4 236
 
1.3%
6 229
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14944
80.0%
Dash Punctuation 3736
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4533
30.3%
0 4391
29.4%
1 3296
22.1%
9 607
 
4.1%
8 601
 
4.0%
7 537
 
3.6%
3 340
 
2.3%
4 236
 
1.6%
6 229
 
1.5%
5 174
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 3736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18680
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4533
24.3%
0 4391
23.5%
- 3736
20.0%
1 3296
17.6%
9 607
 
3.2%
8 601
 
3.2%
7 537
 
2.9%
3 340
 
1.8%
4 236
 
1.3%
6 229
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4533
24.3%
0 4391
23.5%
- 3736
20.0%
1 3296
17.6%
9 607
 
3.2%
8 601
 
3.2%
7 537
 
2.9%
3 340
 
1.8%
4 236
 
1.3%
6 229
 
1.2%

Interactions

2023-02-14T14:22:50.784293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:34.479141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.512327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.518792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:37.978970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.923186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.875053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.155644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:42.713782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.255919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:45.324884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.927512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:48.885989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:50.949096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:34.550347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.595048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.616662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.053230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.981068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.958756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.232347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:42.924644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.331520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:45.400033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:47.111838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:49.086475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:51.046197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:34.649374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.661593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.709602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.159023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.049069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.030782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.302185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:43.007726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.427443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:45.473902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:47.293896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:49.288369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:51.145605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:34.708925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.728308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.777539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.226679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.110925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.097518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.432026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:43.225158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.531641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:45.990121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:47.451187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:49.416903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:51.235921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:34.804267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.797276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.864564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.291318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.187288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.167115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.621241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:43.498156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.601125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.065247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:47.544537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:49.534450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:51.499804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:34.888703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.862322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.946676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.365912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.282909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.236188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.709684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:43.617695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.661695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.139814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:47.699550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:49.713614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:51.638600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:34.990846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.933190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:37.039357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.440973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.357413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.313662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.802853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:43.770790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.738511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.224094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:47.928818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:49.842652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:51.803239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.065966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.995847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:37.137791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.519870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.425438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.407218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.897505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:43.834936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.818615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.302457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:48.113745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:50.051709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:51.960003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.140030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.057741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:37.214665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.577713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.504670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.566330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:42.100321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:43.899294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.946682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.438938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:48.243078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:50.231288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:52.063748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.229260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.118729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:37.283598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.661834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.595787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.710581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:42.274586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:43.972342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:45.055504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.564408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:48.325207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:50.351839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:52.295974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.294169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.215771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:37.377433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.739471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.669455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.909807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:42.347823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.051565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:45.121878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.641330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:48.442552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:50.455509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:52.366799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.363413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.330709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:37.537588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.799319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.730641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:40.990669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:42.491937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.115699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:45.185909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.725179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:48.618380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:50.551427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:52.493225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:35.431661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:36.433136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:37.922071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:38.865070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:39.805820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:41.068516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:42.629864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:44.187694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:45.258236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:46.792914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:48.764029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:22:50.672077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-14T14:22:59.676317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
DaysBlockSoldLeftOcc.(%)Block1Sold1Left1Occ.(%)1Occ.NettoProfitprıceOriginday_nameflight_monthseasonFlight CodeAirline Companydptdpt1Netto Currency
Days1.000-0.008-0.010-0.0290.038-0.0110.000-0.0790.0850.034-0.0520.025-0.0160.2171.0000.0000.0000.3270.0720.0520.1210.000
Block-0.0081.0000.9230.367-0.1190.9850.8850.1230.024-0.212-0.656-0.348-0.6690.4700.0930.2000.2320.7720.4110.2300.3160.516
Sold-0.0100.9231.0000.1340.1170.9140.8080.175-0.0310.021-0.605-0.302-0.5860.3250.0740.1730.2060.4900.3380.2140.3010.480
Left-0.0290.3670.1341.000-0.9030.3570.343-0.0450.088-0.960-0.301-0.326-0.4370.0600.0000.2330.1810.2200.0680.1250.0660.116
Occ.(%)0.038-0.1190.117-0.9031.000-0.110-0.1260.095-0.1050.9570.1120.2620.2730.0660.0000.2260.1890.2040.0520.1140.0500.111
Block1-0.0110.9850.9140.357-0.1101.0000.9000.1270.025-0.202-0.658-0.335-0.6630.4570.0930.2010.2330.7640.4080.2290.3140.517
Sold10.0000.8850.8080.343-0.1260.9001.000-0.1560.304-0.208-0.579-0.367-0.6390.3260.0810.1670.2230.4910.3370.2060.2850.473
Left1-0.0790.1230.175-0.0450.0950.127-0.1561.000-0.8990.081-0.1670.004-0.0920.0680.0000.2440.1570.2360.0590.0520.0900.131
Occ.(%)10.0850.024-0.0310.088-0.1050.0250.304-0.8991.000-0.1030.065-0.047-0.0070.0550.0450.2150.1680.1840.0590.0470.0600.107
Occ.0.034-0.2120.021-0.9600.957-0.202-0.2080.081-0.1031.0000.1750.2800.3260.0630.0000.2310.1970.2040.0540.1180.0460.111
Netto-0.052-0.656-0.605-0.3010.112-0.658-0.579-0.1670.0650.1751.0000.2380.7690.3580.0550.2610.4010.7220.5370.4680.4730.994
Profit0.025-0.348-0.302-0.3260.262-0.335-0.3670.004-0.0470.2800.2381.0000.7580.1020.0000.1750.1970.1860.1430.1670.1650.145
prıce-0.016-0.669-0.586-0.4370.273-0.663-0.639-0.092-0.0070.3260.7690.7581.0000.1790.0280.2390.3170.3190.2630.2250.2600.521
Origin0.2170.4700.3250.0600.0660.4570.3260.0680.0550.0630.3580.1020.1791.0000.2170.0890.1150.9820.4810.4020.4990.280
day_name1.0000.0930.0740.0000.0000.0930.0810.0000.0450.0000.0550.0000.0280.2171.0000.0000.0000.3270.0720.0520.1210.000
flight_month0.0000.2000.1730.2330.2260.2010.1670.2440.2150.2310.2610.1750.2390.0890.0001.0000.9980.3450.2280.2230.2610.199
season0.0000.2320.2060.1810.1890.2330.2230.1570.1680.1970.4010.1970.3170.1150.0000.9981.0000.4860.3060.2030.2180.150
Flight Code0.3270.7720.4900.2200.2040.7640.4910.2360.1840.2040.7220.1860.3190.9820.3270.3450.4861.0000.9790.7890.7810.977
Airline Company0.0720.4110.3380.0680.0520.4080.3370.0590.0590.0540.5370.1430.2630.4810.0720.2280.3060.9791.0000.4250.5340.994
dpt0.0520.2300.2140.1250.1140.2290.2060.0520.0470.1180.4680.1670.2250.4020.0520.2230.2030.7890.4251.0000.3870.565
dpt10.1210.3160.3010.0660.0500.3140.2850.0900.0600.0460.4730.1650.2600.4990.1210.2610.2180.7810.5340.3871.0000.622
Netto Currency0.0000.5160.4800.1160.1110.5170.4730.1310.1070.1110.9940.1450.5210.2800.0000.1990.1500.9770.9940.5650.6221.000

Missing values

2023-02-14T14:22:52.736860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-14T14:22:53.006157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-14T14:22:53.149984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DestinationOriginTo AreaFlight Dateday_nameflight_monthseasonyearFlight CodeDaysAirline CompanydptBlockSoldLeftOcc.(%)dpt1Block1Sold1Left1Occ.(%)1Occ.NettoNetto CurrencyProfitprıceday_convert
0TurkeyArkhangelskAntalya03.08.2021TuesdayAugustSummer2021U6 11372Ural Airlinesafternoon2202200100morning22032171100.00257.0EUR-3.65253.352021-08-03
1TurkeyArkhangelskAntalya06.08.2021FridayAugustSummer2021U6 11375Ural Airlinesmorning2202200100night22002200100.00257.0EUR31.46288.462021-08-06
2TurkeyArkhangelskAntalya10.08.2021TuesdayAugustSummer2021U6 11372Ural Airlinesafternoon2202191100morning220591612799.55262.0EUR40.54302.542021-08-10
3TurkeyArkhangelskAntalya13.08.2021FridayAugustSummer2021U6 11375Ural Airlinesmorning2202191100night220194268899.55273.0EUR31.34304.342021-08-13
4TurkeyArkhangelskAntalya17.08.2021TuesdayAugustSummer2021U6 11372Ural Airlinesafternoon2202200100morning2201685276100.00272.0EUR41.66313.662021-08-17
5TurkeyArkhangelskAntalya20.08.2021FridayAugustSummer2021U6 11375Ural Airlinesafternoon2202200100night2202200100100.00277.0EUR51.56328.562021-08-20
6TurkeyArkhangelskAntalya22.08.2021SundayAugustSummer2021WZ 40377Red Wings Airlinesafternoon2202200100morning220152057100.00261.0EUR-14.38246.622021-08-22
7TurkeyArkhangelskAntalya24.08.2021TuesdayAugustSummer2021U6 11372Ural Airlinesafternoon2202200100morning2202200100100.00277.0EUR-16.86260.142021-08-24
8TurkeyArkhangelskAntalya27.08.2021FridayAugustSummer2021U6 11375Ural Airlinesmorning2202200100night2202200100100.00277.0EUR-51.27225.732021-08-27
9TurkeyArkhangelskAntalya29.08.2021SundayAugustSummer2021WZ 40377Red Wings Airlinesafternoon2202200100morning2202200100100.00278.0EUR-68.98209.022021-08-29
DestinationOriginTo AreaFlight Dateday_nameflight_monthseasonyearFlight CodeDaysAirline CompanydptBlockSoldLeftOcc.(%)dpt1Block1Sold1Left1Occ.(%)1Occ.NettoNetto CurrencyProfitprıceday_convert
1858TurkeyKrasnodarAntalya27.10.2021WednesdayOctoberFall2021U6 11753Ural Airlinesmorning2205017023morning220220010022.73156.0EUR-4.93151.072021-10-27
1859TurkeyKrasnodarAntalya30.10.2021SaturdayOctoberFall2021U6 11756Ural Airlinesmorning2202200100night2202200100100.00159.0EUR21.43180.432021-10-30
1860TurkeyKrasnodarAntalya06.11.2021SaturdayNovemberFall2021U6 11756Ural Airlinesafternoon2205716326morning220220010025.91173.0EUR-150.2522.752021-11-06
1861TurkeyUfaAntalya24.06.2021ThursdayJuneSummer2021RL 80174Royal Flightafternoon2352350100morning23502350100.00217.0EUR68.09285.092021-06-24
1862TurkeyUfaAntalya27.06.2021SundayJuneSummer2021RL 80177Royal Flightafternoon235230598morning2350235097.87217.0EUR47.68264.682021-06-27
1863TurkeyUfaAntalya28.06.2021MondayJuneSummer2021RL 80071Royal Flightnight235228797evening2351234097.02215.0EUR45.81260.812021-06-28
1864TurkeyUfaAntalya04.07.2021SundayJulySummer2021RL 80077Royal Flightnight2352341100evening235174617499.57238.0EUR17.91255.912021-07-04
1865TurkeyUfaAntalya05.07.2021MondayJulySummer2021RL 80071Royal Flightnight2352350100evening2352211494100.00234.0EUR19.32253.322021-07-05
1866TurkeyUfaAntalya09.07.2021FridayJulySummer2021RL 80075Royal Flightafternoon2352181793morning235235010092.77239.0EUR-22.59216.412021-07-09
1867TurkeyUfaAntalya29.07.2021ThursdayJulySummer2021U6 10074Ural Airlinesevening2202002091afternoon220220010090.91256.0EUR-39.70216.302021-07-29